Name
Title | ||
|---|---|---|
Robust Adaptive Dead Zone Technology for Fault-Tolerant Control of Robot Manipulators Using Neural Networks |
Abstract | ||
|---|---|---|
In this paper, a multi-layered feed-forward neural network is trained on-line by robust adaptive dead zone scheme to identify simulated faults occurring in the robot system and reconfigure the control law to prevent the tracking performance from deteriorating in the presence of system uncertainty. Consider the fact that system uncertainty can not be known a priori, the proposed robust adaptive dead zone scheme can estimate the upper bound of system uncertainty on line to ensure convergence of the training algorithm, in turn the stability of the control system. A discrete-time robust weight-tuning algorithm using the adaptive dead zone scheme is presented with a complete convergence proof. The effectiveness of the proposed methodology has been shown by simulations for a two-link robot manipulator. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1023/A:1014603028024 | Journal of Intelligent and Robotic Systems |
Keywords | Field | DocType |
neural networks,robotic fault-tolerant control,adaptive dead zone | Convergence (routing),Dead zone,Control theory,Upper and lower bounds,A priori and a posteriori,Control engineering,Fault tolerance,Control system,Engineering,Adaptive control,Artificial neural network | Journal |
Volume | Issue | ISSN |
33 | 2 | 1573-0409 |
Citations | PageRank | References |
12 | 0.81 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Q. Song | 1 | 65 | 6.02 |
| Wenjie J. Hu | 2 | 24 | 1.61 |
| L. Yin | 3 | 12 | 1.15 |
| Yeng Chai Soh | 4 | 1777 | 155.44 |